Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Applicant’s amendments filed on 03/11/2026 have been received and considered. Claims 1-20 are pending. Claims 1, 19, and 20 have been amended. No new claims have been added or cancelled.
Specification
The objections to paragraphs 35 and 43 due to minor informalities are withdrawn in view of the Applicant’s amendments to the specification.
Response to Arguments
Applicant’s arguments, see pg. 8, filed 03/11/2026, with respect to the rejection under 35 U.S. 101 for claim 20 have been withdrawn in view of the Applicant’s amendment to recite “non-transitory”.
Applicant’s arguments, see pg. 9-10, filed 03/11/2026, with respect to the rejection of claim 1, 19, and 20 under 35 U.S.C. 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Kurita et al. (U.S. 2021/0152749 A1, hereinafter Kurita).
The Applicant argues that Wang does not teach “without a need to predict an albedo for the input image” (Remarks, pg. 10, full par. 1, lines 5-6). The Examiner agrees, however, upon further consideration, a new ground of rejection is made for claim 1 in view of Kurita, as fully explained below.
Claims 19 and 20, which have been amended in a similar manner, see Remarks, pg. 11, filed 03/11/2026, are also rejected under the same new ground of rejection as claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 11, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“Single Image Portrait Relighting via Explicit Multiple Reflectance Channel Modeling”, hereinafter Wang) in view of Kurita. (U.S. 2021/0152749 A1)
Regarding claim 1, Wang teaches a computer-implemented method, comprising: ([pg. 2, col. 2, par. 2, lines 10-13] “method… to achieve authentic lighting effects, especially for specular and shadow.”)
receiving, by a computing device, an input image comprising a subject; ([Fig. 4, lines 1-2] “network architecture… given an input portrait”)
adjusting, by a neural network, one or more of a specular component or a diffuse component associated with the input image, ([pg. 12, col. 1, par. 1, lines 5-7] “With the help of our SS network, our method can control the specular and shadow in the relit images. By manipulating the scale of the generated specular and shadow components”, also seen in Fig. 4 by the SS network.)
wherein the neural network has been configured to redistribute a per-pixel light energy of the input image; ([pg. 12, col. 2, 2nd full paragraph, lines 1-10] “single image portrait relighting framework by explicit modeling multiple reflectance channels which embed… lighting effects of specular and shadow… blends the features of the multiple reflectance channels more consistently, thus achieving photo-realistic relit results”, where “the environment lighting is estimated… [where s]ince an environment map is mapped to a sphere for rendering, different pixels correspond to different region sizes on a sphere” [pg. 6, col. 2, 1st full paragraph, lines 8-10]). Also seen in Fig. 4 between the input and relighted images.)
and predicting, by the neural network, an output image comprising the subject with the adjusted one or more of the specular component or the diffuse component ([pg. 5, Fig. 4, lines 2-3] “a Specular and Shadow (SS) network to explicitly predict challenging lighting effects, i.e., specular and shadow… to generate the relit results”, also Fig. 4 as the relighted image.)
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Fig. 4 (Wang et al.)
Wang fails to teach without a need to predict an albedo for the input image, by reducing a specular highlight associated with the subject or reducing a per-pixel light energy of a shadow region of the input image. However, this is known in the art as taught by Kurita.
Kurita teaches without a need to predict an albedo for the input image, by reducing a specular highlight associated with the subject or reducing a per-pixel light energy of a shadow region of the input image (FIG. 11(a)-(e), where “(a) of FIG. 11 illustrates a normal image” [0087], “A gain setting unit 331-1 of the target image generation unit 33-1 performs level adjustment of the specular reflection image with the gain set by use of a learned model on the basis of the specular reflection image and the diffuse reflection image.” [0088], “the specular reflection image shown in (c) of FIG. 11 turns to, for example, a specular reflection image shown in (d) of FIG. 11 after the level adjustment” [0088], and “The learned model generation unit 52-1 sets, for each pixel, gain for the specular reflection image on the basis of the specular reflection image and the diffuse reflection image by using a learning model… The learned model generation unit 52-1 uses, as a learning model, a deep learning model such as a convolutional neural network (CNN)” [0092]).
Kurita is analogous to the claimed invention, as both relate to neural networks adjusting the specular component of a portrait image. Kurita further teaches that “the learning apparatus generates a learned model. Thus, it is possible to easily obtain a target image from a polarization image” [0208], where “the gain for a specular reflection component is adjusted to generate a target image” [0105]. Therefore, it would be obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kurita to Wang in order to easily create an output image with adjusted specular components.
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FIG. 11 (Kurita et al.)
Regarding claim 2, the combination of Wang and Kurita teaches the computer-implemented method of claim 1, wherein the adjusting of one or more of the specular component or the diffuse component comprises adjusting of the specular component, (Wang; [pg. 12, Fig. 12, line 1] “specular components manipulation in the relit images”)
and wherein the redistributing of the per-pixel light energy comprises reducing a specular highlight associated with the subject ([pg. 4, col. 2, par. 2, lines 5-8] “we empirically set the specular… to 0.6… In addition, we set the specular coefficient to 0 to further render an image without specular, where “the environment lighting is estimated… where 𝜔 is the solid angle of a pixel in the environment map” [pg. 6, col. 2, 1st full paragraph, line 7]).
Regarding claim 3, the combination of Wang and Kurita teaches the computer-implemented method of claim 1, wherein the adjusting of the one or more of the specular component or the diffuse component comprises adjusting of the diffuse component, (Wang; [pg. 12, Fig. 12, line 1] “Shadow… components manipulation in the relit images”. Note: the applicant uses diffuse component and shadow component interchangeably.)
and wherein the redistributing of the per-pixel light energy comprises reducing a per-pixel light energy of a shadow region of the input image. ([pg. 4, col. 2, par. 2, lines 8-9] “By setting the shadow visibility to false in Blender, we render an image without self-occlusions”).
Regarding claim 4, the combination of Wang and Kurita teaches the computer-implemented method of claim 1, but fails to teach further comprising: maintaining, via the neural network, an average of global color values associated with the input image (Kurita; [0087] “the normal image based on a polarization image… is an average value in all polarization directions for each color component at each pixel position”, in which “The polarization image is an image captured by use of, for example, polarized illumination light” [0008] and is used as the input).
Kurita is analogous to the claimed invention, as both relate to a neural network on an input image and adjust the specular and diffuse reflection levels. Kurita further teaches that the apparatus “a target image can be easily obtained from a polarization image” [0017], where “a target image is generated from the level-adjusted component image” [0017] and the “the component images are, for example, a specular reflection image and a diffuse reflection image” [0009]. Therefore, it would be obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kurita to the combination of Wang and Kurita in order to adjust the specular or diffuse components of the input image, which are easily obtained by having the average of all color values.
Regarding claim 5, the combination of Wang and Kurita teaches the computer-implemented method of claim 1, further comprising: predicting one or more characteristics of a color scheme associated with the specular component (Wang; [pg. 6, col. 1, 1st full paragraph, lines 4-5] “we first use a de-lighting network to estimate the facial albedo”. Note: the examiner interprets the albedo image to be the color scheme, as the examiner interprets “color scheme” as “the color underneath the specular highlight” as stated in the applicant’s specifications in paragraph [48]).
Regarding claim 6, the combination of Wang and Kurita teaches the computer-implemented method of claim 1, wherein the input image is a portrait of the subject (Wang; [Fig. 4, line 2] “given an input portrait”).
Regarding claim 11, the combination of Wang and Kurita teaches the computer-implemented method of claim 1, further comprising: training the neural network to receive a particular input image with a particular subject, (Wang; [pg. 6, col. 1, par. 2, lines 12-13] “we train a composition network that takes facial albedo”, as seen in Fig. 4)
and predict a particular output image comprising the subject with a particular adjusted one or more of the specular component or the diffuse component ([pg. 6, col. 1, par. 2, lines 13-14] “normal and the estimated lighting effects as inputs to perform the final creation of the relit results.”).
Regarding claim 17, the combination of Wang and Kurita further teaches the computer-implemented method of claim 1, further comprising: providing the output image as an input to another neural network configured to perform image relighting (Wang; [pg. 8, col. 1, par. 3, lines 1-2] “the inputs of the SS and composition networks are the predictions of our networks”).
Regarding claim 18, claim 18 has substantially similar limitations to claim 11, therefore, will be rejected using the same rationale. the combination of Wang and Kurita further teaches configured to perform portrait background replacement (Wang; [pg. 10, col. 1, par. 2, lines 3-6] “To improve the visual effects, we follow the method [Sun et al. 2019] to change the background of an input image to the corresponding part of the environment lighting map in the results.”, also seen in Fig. 11. Note: Fig. 11 is too large to fit into the document and will not be added).
Regarding claim 19, claim 19 teaches substantially similar limitations to claim 1, but in a computing device. Wang does not explicitly teach a computing device, comprising: one or more processors; and data storage, wherein the data storage has stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing device to carry out functions. However, this is known in the art as taught by Kurita.
Kurita teaches a computing device, comprising: one or more processors; and data storage, wherein the data storage has stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing device to carry out functions (“Note that the program of the present technology is, for example, a program that can be provided to a general-purpose computer capable of executing various program codes” [0014]). As mentioned before, Kurita is analogous to the claimed invention. Kurita further teaches that “the present technology is suitable to, for example… various devices using polarization information and a high-texture image” [0208]. Therefore, it would be obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kurita to Wang, as Kurita teaches that a neural network adjusting the specular and diffuse components of an image can be stored on instructions for a computing device to carry out.
Regarding claim 20, claim 20 has substantially similar limitations to claim 1, but as computer readable media. Wang does not explicitly teach an article of manufacture comprising one or more computer readable media having computer-readable instructions stored thereon that, when executed by one or more processors of a computing device, cause the computing device to carry out functions. However, this is known in the art as taught by Kurita.
Kurita teaches an article of manufacture comprising one or more computer readable media having computer-readable instructions stored thereon that, when executed by one or more processors of a computing device, cause the computing device to carry out functions ([0014] “As a result of providing such a program in a computer-readable form, a process corresponding to the program is implemented on a computer.”). Similarly to claim 19, it would be obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kurita to Wang, as Kurita teaches that the same neural network can have instructions stored to be used by the computing device.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“Single Image Portrait Relighting via Explicit Multiple Reflectance Channel Modeling”) in view of Kurita (U.S. 2021/0152749 A1), and further in view of Isola et al. (“Image-to-Image Translation with Conditional Adversarial Networks”, hereinafter Isola).
The combination of Wang and Kurita teaches the computer-implemented method of claim 1, but does not explicitly teach wherein the neural network comprises a U-net architecture configured to maintain high frequency aspects of the input image. However, Wang does teach “The skip connection… is employed to preserve the image details of the input” [pg. 6, col. 2, par. 1, lines 1-3], in which the “skip connections follow the general shape of a ‘U-Net’” [pg. 3, col. 2, par. 4, lines 2-3] as taught by Isola. Isola is analogous to the claimed invention, as both relate to training a neural network to translate an input image into a corresponding output image by mapping pixels and is not limited to relighting images, which can be seen in Figure 1 as shown below. Isola further teaches that “Adding skip connections… to create a ‘U-Net’ results in much higher quality results” [pg. 5, Figure 5] Therefore, it would be obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Isola to the combination of Wang and Kurita in order to produce a higher quality output image after image translation.
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Figure 1 (Isola et al.)
Claims 8-10, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“Single Image Portrait Relighting via Explicit Multiple Reflectance Channel Modeling”) in view of Kurita et al. (U.S. 2021/0152749 A1), and further in view of Marin et al. (US 2018/0047208, hereinafter Marin).
Regarding claim 8, the combination of Wang and Kurita teaches the computer-implemented method of claim 1, but fails to teach further comprising: providing, by a graphical user interface of the computing device, a user-adjustable slider bar to indicate an amount of the adjusting of the one or more of the specular component or the diffuse component; receiving, by the graphical user interface, a user-indication of the amount of the adjusting of the one or more of the specular component or the diffuse component; and providing, by the graphical user interface, the output image based on the user indicated amount of the adjusting.
However, this is known in the art as taught by Marin. Marin teaches providing, by a graphical user interface of the computing device, a user-adjustable slider bar to indicate an amount of the adjusting of the one or more of the specular component or the diffuse component; ([0120] “a user interface for the user to manually control the parameters of the specular component of the reduced BRDF model… and a user interface control such as a numerical input box, a slider, or a knob may be used to change the values of each of the parameters.”)
receiving, by the graphical user interface, a user-indication of the amount of the adjusting of the one or more of the specular component or the diffuse component; ([0143] “scanning system may display user interface controls and accept user input for specifying the parameters of the BDRF”)
and providing, by the graphical user interface, the output image based on the user indicated amount of the adjusting ([0120] “the scanning system 100 displays a rendering of the 3D model based on the diffuse component of the BRDF, and further provides a user interface”).
Marin is analogous to the claimed invention, as both relate to a neural network adjusting the specular component of an input image using user input. Marin further teaches that the user interface allows the user to “manually control the parameters of the specular component… so that the rendered images closely reproduce the characteristics of the surface” [0120]. Marin also teaches the interface displays both the input and output image, such that “the user can manually compare the appearance of the rendered model with the captured images”. Therefore, it would be obvious for one of ordinary skill in the art before the effective filing date of claimed invention to incorporate the teachings of Marin to the combination of Wang and Kurita to allow the user to view and control the specular component manually to their preference.
Regarding claim 9, the combination of Wang, Kurita, and Marin teaches the computer-implemented method of claim 8, wherein the providing of the output image comprises applying a linear interpolation of the input image and the output image, and wherein the linear interpolation is based on the user-indication, via the slider as taught by Marin. A slider is a visualized way to show linear interpolation between two points, in which the slider taught by Marin is the linear interpolation between the input image, where there is no change to the parameters, and the output image, wherein any amount of specular component is changed.
Regarding claim 10, the combination of Wang, Kurita, and Marin teaches the computer-implemented method of claim 8, wherein the adjusting of the one or more of the specular component or the diffuse component comprises predicting, by the neural network, the output image based on the user indicated amount of the adjusting (Wang; [0120] “With the help of our SS network, our method can control the specular and shadow in the relit images… our method enables users to adjust the relighting effects”).
Regarding claim 12, the combination of Wang and Kurita teaches the computer-implemented method of claim 11, wherein a training dataset comprises a plurality of image pairs, wherein a first image of a given image pair comprises a subject in a lighting environment (Wang; [pg. 8, col. 1, par. 1, line 5] “In training our de-lighting network, the ground-truth facial albedo, normal and parsing map are provided from one image group”, where “Recall that each image group is captured under one lighting” [pg. 8, col. 1, par. 2, line 1]),
and wherein a second image of the given image pair comprises the subject (Wang; [pg. 8, col. 1, par. 2, lines 2-3] “In order to fine-tune our entire network, we sample another image group capturing the same subject in the same pose but a different lighting”).
The combination of Wang and Kurita does teach the other image pair to be under a different lighting, but fails to teach wherein a second image of the given image pair comprises the subject in a diffused version of the lighting environment. However, this is taught by Marin. Marin teaches an image where the picture is taken under diffuse lighting ([0051] “FIG. 1C depicts the shoe indoors under diffuse lighting”). Marin explains that this is done “specular reflections (e.g., specular highlights) captured during the scan may be stored (or “baked-in”) to the texture information”, and that “fewer or no such specular highlights appear when the shoe is under diffuse light”. Therefore, it would be obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Marin to the combination of Wang and Kurita to further train the neural network using images of the subject without specular highlights, which is done by the subject being under a diffused lighting environment.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“Single Image Portrait Relighting via Explicit Multiple Reflectance Channel Modeling”) in view of Kurita et al. (U.S. 2021/0152749 A1) and Marin et al. (US 2018/0047208), and further in view of VGL (“HDR Shop Support”).
The combination of Wang, Kurita, and Marin teaches the computer-implemented method of claim 12, but fails to teach wherein the lighting environment is a high dynamic range lighting environment, and wherein the training comprises generating a specular convolution of a portion of the high dynamic range lighting environment. However, this is known in the art as taught by VGL.
VGL teaches wherein the lighting environment is a high dynamic range lighting environment ([“Creating a Light Probe Image”, par. 1, line 1] “A light probe is an omni-directional (360° panoramic) high-dynamic range image”)
and wherein the training comprises generating a specular convolution of a portion of the high dynamic range lighting environment ([“Diffuse and Specular Convolution”, par. 1, line 1] “HDRshop can perform a diffuse or specular convolution on a high-dynamic range 360 degree panoramic image (also called a light probe).”).
VGL further teaches that a high dynamic range image is used as a lighting environment “to provide interesting and realistic lighting environments and backgrounds for rendered graphics” [“Creating a Light Probe Image”, par. 1, lines 2-3]. VGL also teaches that generating a specular convolution of the high dynamic range lighting environment is “useful if you need to pre-compute a diffuse or rough specular texture map; for example, to light an object using a light probe in real time applications” [“Diffuse and Specular Convolution”, par. 1, lines 1-2]. Therefore, it would be obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of VGL to the combination of Wang, Kurita, and Marin to use a high dynamic range lighting environment for more realistic lighting environments in real time.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“Single Image Portrait Relighting via Explicit Multiple Reflectance Channel Modeling”) in view of Kurita et al. (U.S. 2021/0152749 A1), Marin et al. (US 2018/0047208), and VGL (“HDR Shop Support”), and further in view of Boom et al. (“Model-Based Illumination Correction for Face Images in Uncontrolled Scenarios”, hereinafter Boom).
The combination of Wang, Kurita, Marin, and VGL teaches the computer-implemented method of claim 13, but fails to teach wherein the generating of the specular convolution comprises applying a Phong Reflectance Model. However, this is taught in the art by Boom. Boom teaches “We use the Phong model which allows us to model ambient light in shadow areas” [Abstract, lines 5-6]. Boom is analogous to the claimed invention as well, as it relates to face image relighting, and teaches a method that “correct[s] for these illumination variations in a single face image”. Therefore, it would be obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to use the Phong reflectance model to be able to correct illumination variance in a face image by modeling ambient light.
Claims 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“Single Image Portrait Relighting via Explicit Multiple Reflectance Channel Modeling”) in view of Kurita et al. (U.S. 2021/0152749 A1), and further in view of Zhou et al. (“Deep Single-Image Portrait Relighting”, hereinafter Zhou).
In regards to claim 15, the combination of Wang and Kurita teaches the computer-implemented method of claim 11, but fails to teach wherein the training comprises applying an adversarial loss function to a selected portion of the subject. However, this is known in the art as taught by Zhou. Zhou teaches that an adversarial loss function ([Abstract, lines 13-14] “A GAN loss”, where the GAN loss function is seen in equation 4 [pg. 7197]) is “applied to improve the quality of the relit portrait image” [Abstract, lines 14-15]. Zhou is also analogous to the claimed invention, as both relate to the relighting of portraits. Therefore, it would be obvious for one of ordinary skill in the art before the effective date of the claimed invention to incorporate the teachings of Zhou to Wang to be able produce higher quality relit portraits.
In regards to claim 16, the combination of Wang, Kurita, and Zhou teaches the computer-implemented method of claim 15, wherein the selected portion is a face portion of the subject (Zhou; [Abstract, lines 13-15] “A GAN loss is further applied to improve the quality of the relit portrait image”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Regarding claim 9, the examiner uses the teachings of Stobbe to define and connect linear interpolation with the user-adjustable slider. Stobbe describes linear interpolation to as finding an unknown value in between two known values. Stobbe includes a visualization tool where the user can drag a point between two points, in which the tool is a slider between two points that the user can set. Stobbe also gives the explanation that “smoothly transition[ing] from darkness to light” [par. 1, line 3] is an example of linear interpolation in computer graphics.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALICIA HA whose telephone number is (571)272-3601. The examiner can normally be reached Mon-Thurs 9:00 AM - 6:00 PM, and Fri 9:00 AM - 1:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kee Tung can be reached at (571) 272-7794. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611
/ALICIA HA/Examiner, Art Unit 2611
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